|
|
The Publications
Result of the query in the list of publications :
245 Conference articles |
119 - Automatic 3D Building Reconstruction from DEMs: an Application to PLEIADES Simulations.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. International Society for Photogrammetry and Remote Sensing Commission I Symposium (ISPRS), Marne La Vallee, France, July 2006.
Keywords : 3D reconstruction, Digital Elevation Model, Building extraction, Dense urban areas, PLEIADES simulations.
@INPROCEEDINGS{lafarge_isprs06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Automatic 3D Building Reconstruction from DEMs: an Application to PLEIADES Simulations}, |
| year |
= |
{2006}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. International Society for Photogrammetry and Remote Sensing Commission I Symposium (ISPRS)}, |
| address |
= |
{Marne La Vallee, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_isprs06.pdf}, |
| keyword |
= |
{3D reconstruction, Digital Elevation Model, Building extraction, Dense urban areas, PLEIADES simulations} |
| } |
|
120 - A comparative study of three methods for identifying individual tree crowns in aerial images covering different types of forests.
M. Eriksson and G. Perrin and X. Descombes and J. Zerubia. In Proc. International Society for Photogrammetry and Remote Sensing (ISPRS), Marne La Vallee, France, July 2006.
Keywords : Region Growing, Marked point process, Markov Fields, Object extraction, Tree Crown Extraction.
@INPROCEEDINGS{eriksson06a,
|
| author |
= |
{Eriksson, M. and Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A comparative study of three methods for identifying individual tree crowns in aerial images covering different types of forests}, |
| year |
= |
{2006}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. International Society for Photogrammetry and Remote Sensing (ISPRS)}, |
| address |
= |
{Marne La Vallee, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_eriksson06a.pdf}, |
| keyword |
= |
{Region Growing, Marked point process, Markov Fields, Object extraction, Tree Crown Extraction} |
| } |
Abstract :
Most of today's silviculture methods has the goal to optimise the outcome of the forest in stem volume when it is cut. It might also be relevant to save parts of the forest, for instance, to protect a habitat. In order to get a good survey of the forest, remote sensed images are often used. These images are most often manually interpreted in combination with field measurements in order to estimate the forest parameters that are of importance in the decision how to optimally maintain the forest. Among these parameters the most common are stem number, stem volume, and tree species. Interpretation of images are often labour and time consuming. Thus, automatically developed methods for interpretation can lower the work load and speed up the interpretation time.
The interpretation is often done using images captured from a far distance from the ground in order to capture as large area as possible. However, this lower the accuracy of the estimates since it must be done stand wise. Knowledge of where each individual trees in the forest is located together with its size will increase accuracy. It makes it also possible to plan the cutting in detail. With this knowledge in mind, research about finding automatically methods for finding individual tree crowns in aerial images has been a subject for researchers the last decades.
Today's methods are not capable to alone handle all kind of forests. Therefore, comparative studies of different segmentation methods with different types of forests are of importance in order to clarify how much a method is reliable at a certain type of forest. This knowledge can, for instance, be used to build up an expert system which are supposed to be able to find individual tree crowns in any kind of forests. The comparison is done using images covering different types of forests. The types of forests that are included in the study ranges from isolated tree crown where the ground is clearly visible between the crowns to dense forest which is naturally regenerated via planted forest.
In this study we compare three existing segmentation methods for extracting individual tree crowns from aerial images. The first two methods are probabilistic methods which minimises some energy function while the third is a region growing algorithm. The first probabilistic method is based on a Markov Random Field modelling. We define a prior Markov model to segment the image into three classes (background, vegetation and tree centres). The prior model embed a circular shape model of the tree crown with a random radius. The data term allows to well position the tree centres onto the image and to describe the tree shape as fluctuations around the circular template. Besides, some long range interactions models the relations between the trees locations, such as some periodicity in case of plantations.
The second probabilistic method consists in modeling the trees in the forestry images as random configurations of ellipses or ellipsoids, whose points are the positions of the stems and marks their geometric features. The density of this process embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted. We estimate the best configuration of an unknown number of objects, from which 2D and 3D vegetation resource parameters can be extracted. To sample this marked point process, we use Monte Carlo dynamics, while the optimization is performed via a Simulated Annealing algorithm, which results in a fully automatic approach. This approach works well on plantations, where there are high spatial relations between the trees, and on isolated trees where 3D parameters can be extracted, but some difficulties remain in dense areas.
The third method, the region growing algorithm, relies as all region growing methods on good seed points, i.e. in this case approximate locations of the tree crowns. From the seed points the segments are grown according to a grey level value of the neighbouring pixels. The larger the value is the sooner it is connected to the neighbouring segment. The segments stops to grow when all pixels belongs to a segment. This method, contrary the others, will have as a result, segments that have captured the actual shape of the tree crown if the forest is not too sparse. If the forest is too sparse such that the ground is visible, there are problems of finding the seed points. In the cases when the forest is sparse, there are difficulties to separate the tree crowns from the ground. Even if the seed points would be located only at the tree crowns the result will contain a lot of errors since all pixels most belong to a segment, i.e. even the ground pixels must be connected to a segment in this case. |
|
121 - An Automatic 3D City Model : a Bayesian Approach using Satellite Images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toulouse, France, May 2006.
Note : Copyright IEEE
Keywords : 3D reconstruction, Buildings, MCMC, Digital Elevation Model (DEM).
@INPROCEEDINGS{florenticassp06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{An Automatic 3D City Model : a Bayesian Approach using Satellite Images}, |
| year |
= |
{2006}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Toulouse, France}, |
| note |
= |
{Copyright IEEE}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_florenticassp06.pdf}, |
| keyword |
= |
{3D reconstruction, Buildings, MCMC, Digital Elevation Model (DEM)} |
| } |
|
122 - Forest Resource Assessment using Stochastic Geometry.
G. Perrin and X. Descombes and J. Zerubia and J.G. Boureau. In Proc. International Precision Forestry Symposium, March 2006.
Keywords : Tree Crown Extraction, Object extraction, Stochastic geometry, RJMCMC, Data energy.
@INPROCEEDINGS{perrin_06_b,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J. and Boureau, J.G.}, |
| title |
= |
{Forest Resource Assessment using Stochastic Geometry}, |
| year |
= |
{2006}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. International Precision Forestry Symposium}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_ipfs06.pdf}, |
| keyword |
= |
{Tree Crown Extraction, Object extraction, Stochastic geometry, RJMCMC, Data energy} |
| } |
Abstract :
Aerial and satellite imagery has a key role to play in natural resource management, especially in forestry application. The submetric resolution of the data enables to study forests at the scale of trees, and to get a more accurate assessment of the resources such as the number of stems or the forest cover. To develop automatic tools in order to help the inventories in their work and to bring more knowledge about the stands is also nowadays of important economical and environmental concerns.
In this paper, we aim at extracting tree crowns from high resolution aerial Color Infrared images (CIR) of forests using marked point processes. Our approach consists in modelling the trees in the forestry images as random configurations of ellipses, whose points are the positions of the stems and marks their geometric features. The density of this process embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted. Our goal is to find the best configuration of an unknown number of objects, i.e. the configuration that maximizes this density. To sample this marked point process, we use Monte Carlo dynamics while the optimization is performed via a Simulated Annealing algorithm, which results in a fully automatic approach.
We present different models for the data term in order to cope with different kinds of stands : plantations, isolated trees and mixed stands. Results are shown on aerial CIR images provided by the French Forest Inventory (IFN) |
|
123 - A study of Gaussian approximations of fluorescence microscopy PSF models.
B. Zhang and J. Zerubia and J.C. Olivo-Marin. In Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIII of Proc. SPIE, in press, Vol. 6090, San Jose, USA, January 2006.
Copyright : SPIE
@INPROCEEDINGS{zerubia_spie06,
|
| author |
= |
{Zhang, B. and Zerubia, J. and Olivo-Marin, J.C.}, |
| title |
= |
{A study of Gaussian approximations of fluorescence microscopy PSF models}, |
| year |
= |
{2006}, |
| month |
= |
{January}, |
| booktitle |
= |
{Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIII of Proc. SPIE, in press}, |
| volume |
= |
{6090}, |
| address |
= |
{San Jose, USA}, |
| keyword |
= |
{} |
| } |
|
124 - Evaluation des Ressources Forestières à l'aide de Processus Ponctuels Marqués.
G. Perrin and X. Descombes and J. Zerubia. In Proc. Reconnaissance des Formes et Intelligence Artificielle (RFIA), Tours, France, January 2006.
Keywords : Tree Crown Extraction, Stochastic geometry, Marked point process, Object extraction.
@INPROCEEDINGS{perrin_06_a,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Evaluation des Ressources Forestières à l'aide de Processus Ponctuels Marqués}, |
| year |
= |
{2006}, |
| month |
= |
{January}, |
| booktitle |
= |
{Proc. Reconnaissance des Formes et Intelligence Artificielle (RFIA)}, |
| address |
= |
{Tours, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_rfia06.pdf}, |
| keyword |
= |
{Tree Crown Extraction, Stochastic geometry, Marked point process, Object extraction} |
| } |
Résumé :
Les images aériennes et satellitaires jouent un role de plus en plus important dans le domaine de la gestion des ressources naturelles, et en particulier des forêts. Les organismes chargés d'en faire l'inventaire, comme l'Inventaire Forestier National (IFN) en France, s'appuient en effet sur ces images pour observer les différentes espèces d'arbres d'une zone boisée, avant de se rendre sur le terrain pour une étude plus poussée. La résolution submétrique des données permet, en outre, d'entrevoir une étude plus fine, à savoir un comptage à l'arbre près et une classification automatique des houppiers (ensemble des branches et du feuillage d'un arbre). Cette évaluation précise des ressources forestières n'est actuellement pas disponible. Aussi, le développement d'outils automatiques, chargés d'aider les gestionnaires du paysage dans leur travail en leur apportant une connaissance des ressources à l'échelle de l'arbre, se révèle-t-il être d'un intérêt grandissant.L'objectif de notre travail est donc d'extraire des houppiers à partir d'images aériennes de forêts à très haute résolution. Notre approche consiste à modéliser les peuplements forestiers par un processus ponctuel marqué d'ellipses, dont les points représentent les positions des arbres et les marques leurs caractéristiques géométriques. La densité de ce processus comporte une composante de régularisation, dite a priori, qui introduit des interactions entre les objets du processus, ainsi qu'une composante d'attache aux données, afin que les objets du processus se positionnent sur les houppiers que l'on souhaite extraire. Il s'agit de trouver la configuration d'objets, en nombre inconnu a priori, qui maximise cette densité. La simulation de tels processus fait appel aux algorithmes de type Monte Carlo par Chaîne de Markov (MCMC) à sauts réversibles, l'optimisation étant réalisée à l'aide d'un recuit simulé.Nous présentons ici un nouveau modèle d'attache aux données. Contrairement à nos précédents modèles testés sur des plantations, ce modèle n'est plus bayésien puisque le terme d'attache aux données est désormais calculé au niveau des objets et non de l'image. Ceci nous permet de travailler sur des images plus générales, avec des densités d'arbres plus variables. Des résultats obtenus sur des images fournies par l'IFN valident ce modèle. |
Abstract :
Aerial and satellite imagery has a key role to play in natural resources management, especially in forestry application. Indeed, forest inventories, such as the French National Inventory (IFN), refer to these images to analyse the different tree species in a stand, before sending a team on the ground to obtain some more advanced knowledge. Moreover, the submetric resolution of the data enables to study forests at the scale of trees, and also to get a more accurate evaluation of the resources such as the number of stems. It would be also of important economical and environmental concerns to develop automatic tools to analyze and monitor forests.We aim at extracting tree crowns from high resolution aerial images of forests. Our approach consists in modelling the forestry images as realizations of a marked point process of ellipses, whose points are the positions of the trees and marks their geometric features. The density of this process embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted. Our goal is to find the best configuration of an unknown number of objects, i.e. the configuration that maximizes this density. To sample the marked point process, we use Monte Carlo dynamics (Reversible Jump Markov Chain Monte Carlo), while the optimization is performed via a simulated annealing algorithm.We present here a new model for the data term. Contrary to our previous models tested on plantations images, this model is not Bayesian anymore : the data term is calculated for each object and not for the whole image. This enables us to work on more general images, with variable tree crown densities. Example results are shown on aerial images provided by the French Forest Inventory (IFN). |
|
125 - Galaxy filament detection using the Quality candy model.
P. Gernez and X. Descombes and J. Zerubia and E. Slezak and A. Bijaoui. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2006.
Keywords : Marked point process, Quality Candy model, Galaxy Filaments.
@INPROCEEDINGS{gernez06,
|
| author |
= |
{Gernez, P. and Descombes, X. and Zerubia, J. and Slezak, E. and Bijaoui, A.}, |
| title |
= |
{Galaxy filament detection using the Quality candy model}, |
| year |
= |
{2006}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_gernez06.pdf}, |
| keyword |
= |
{Marked point process, Quality Candy model, Galaxy Filaments} |
| } |
|
126 - Point process of segments and rectangles for building extraction from DEM.
M. Ortner and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2006.
Keywords : Stochastic geometry, Buildings.
@INPROCEEDINGS{ortner06,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Point process of segments and rectangles for building extraction from DEM}, |
| year |
= |
{2006}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_ortner06.pdf}, |
| keyword |
= |
{Stochastic geometry, Buildings} |
| } |
|
127 - Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application.
G. Perrin and X. Descombes and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), St Augustine, Florida, USA, November 2005.
Keywords : Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC.
Copyright : Springer Verlag
@INPROCEEDINGS{perrin_emmcvpr05,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application}, |
| year |
= |
{2005}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
| address |
= |
{St Augustine, Florida, USA}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.ps.gz}, |
| keyword |
= |
{Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC} |
| } |
Abstract :
We use marked point processes to detect an unknown number of trees from high resolution aerial images. This is in fact an energy minimization problem, where the energy contains a prior term which takes into account the geometrical properties of the objects, and a data term to match these objects to the image. This stochastic process is simulated via a Reversible Jump Markov Chain Monte Carlo procedure, which embeds a Simulated Annealing scheme to extract the best configuration of objects.
We compare here different cooling schedules of the Simulated Annealing algorithm which could provide some good minimization in a short time. We also study some adaptive proposition kernels. |
|
128 - Phase field models and higher-order active contours.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Computer Vision (ICCV), Beijing, China, October 2005.
Keywords : Active contour, Higher-order, Shape, Line networks, Road network, Phase Field.
@INPROCEEDINGS{rochery_iccv05,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Phase field models and higher-order active contours}, |
| year |
= |
{2005}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Computer Vision (ICCV)}, |
| address |
= |
{Beijing, China}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_iccv05.pdf}, |
| keyword |
= |
{Active contour, Higher-order, Shape, Line networks, Road network, Phase Field} |
| } |
Abstract :
The representation and modelling of regions is an important topic in computer vision. In this paper, we represent a region via a level set of a `phase field' function. The function is not constrained, eg to be a distance function; nevertheless, phase field energies equivalent to classical active contour energies can be defined. They represent an advantageous alternative to other methods: a linear representation space; ease of implementation (a PDE with no reinitialization); neutral initialization; greater topological freedom. We extend the basic phase field model with terms that reproduce `higher-order active contour' energies, a powerful way of including prior geometric knowledge in the active contour framework via nonlocal interactions between contour points. In addition to the above advantages, the phase field greatly simplifies the analysis and implementation of the higher-order terms. We define a phase field model that favours regions composed of thin arms meeting at junctions, combine this with image terms, and apply the model to the extraction of line networks from remote sensing images. |
|
129 - Détection de feux de forêt à partir d'images satellitaires IRT par analyse statistique d'évènements rares.
F. Lafarge and X. Descombes and J. Zerubia and S. Mathieu-Marni. In Proc. GRETSI Symposium on Signal and Image Processing, Louvain-la-Neuve, Belgique, September 2005.
Keywords : Rare event, Forest fires, Gaussian Field.
@INPROCEEDINGS{lafarge_gretsi05,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Mathieu-Marni, S.}, |
| title |
= |
{Détection de feux de forêt à partir d'images satellitaires IRT par analyse statistique d'évènements rares}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Louvain-la-Neuve, Belgique}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_gretsi05.pdf}, |
| keyword |
= |
{Rare event, Forest fires, Gaussian Field} |
| } |
|
130 - New Higher-order Active Contour Energies for Network Extraction.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Gap closure, Shape, Prior, Higher-order, Active contour.
@INPROCEEDINGS{rochery_icip05,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{New Higher-order Active Contour Energies for Network Extraction}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Genoa, Italy}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_icip05.pdf}, |
| keyword |
= |
{Gap closure, Shape, Prior, Higher-order, Active contour} |
| } |
Abstract :
Using the framework of higher-order active contours, we present a new quadratic em continuation energy for the extraction of line networks (e.g. road, hydrographic, vascular) in the presence of occlusions. Occlusions create gaps in the data that frequently translate to gaps in the extracted network. The new energy penalizes earby opposing extremities of the network, and thus favours the closure of the gaps created by occlusions. Nearby opposing extremities are identified using a
sophisticated interaction between pairs of points on the contour. This new model allows the extraction of fully connected networks, even though occlusions violate common assumptions about the homogeneity of the
interior, and high contrast with the exterior, of the network. We present experimental results on real aerial images that demonstrate the effectiveness of the new model for network extraction tasks. |
|
131 - Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, September 2005.
@INPROCEEDINGS{lacoste_eusipco05,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Antalya, Turkey}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7078007}, |
| keyword |
= |
{} |
| } |
|
132 - Application of ant colony optimization to image classification using a Markov model withnonstationary neighborhoods.
S. Le Hegarat-Mascle and A. Kallel and X. Descombes. In Proc. SPIE Symposium on Remote Sensing, Vol. 5982, Bruges, Belgium, September 2005.
@INPROCEEDINGS{mascle_spie_05,
|
| author |
= |
{Le Hegarat-Mascle, S. and Kallel, A. and Descombes, X.}, |
| title |
= |
{Application of ant colony optimization to image classification using a Markov model withnonstationary neighborhoods}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. SPIE Symposium on Remote Sensing}, |
| volume |
= |
{5982}, |
| address |
= |
{Bruges, Belgium}, |
| url |
= |
{http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=879756}, |
| keyword |
= |
{} |
| } |
|
133 - Textural Kernel for SVM Classification in Remote Sensing : Application to Forest Fire Detection and Urban Area Extraction.
F. Lafarge and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Support Vector Machines, Learning base, Markov Fields, Forest fires, Urban areas.
Copyright : IEEE
@INPROCEEDINGS{lafarge_icip05,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Textural Kernel for SVM Classification in Remote Sensing : Application to Forest Fire Detection and Urban Area Extraction}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Genoa, Italy}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_icip05.pdf}, |
| keyword |
= |
{Support Vector Machines, Learning base, Markov Fields, Forest fires, Urban areas} |
| } |
|
134 - Maximum A Posteriori Estimation of Radar Cross Section in SAR Images using the Heavy-Tailed Rayleigh Model.
A. Achim and E.E. Kuruoglu and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, September 2005.
@INPROCEEDINGS{achim_eusipco_05,
|
| author |
= |
{Achim, A. and Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{Maximum A Posteriori Estimation of Radar Cross Section in SAR Images using the Heavy-Tailed Rayleigh Model}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Antalya, Turkey}, |
| pdf |
= |
{http://kilyos.ee.bilkent.edu.tr/~signal/defevent/papers/cr1741.pdf}, |
| keyword |
= |
{} |
| } |
|
135 - Texture-adaptive mother wavelet selection for texture analysis.
G.C.K. Abhayaratne and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Texture, Wavelet packet, Adaptive, Mother.
@INPROCEEDINGS{abhayaratne_icip05,
|
| author |
= |
{Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture-adaptive mother wavelet selection for texture analysis}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Genoa, Italy}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne05icip.pdf}, |
| keyword |
= |
{Texture, Wavelet packet, Adaptive, Mother} |
| } |
Abstract :
Classification results obtained using wavelet-based texture analysis techniques vary with the choice of mother wavelet used in the methodology. We discuss the use of mother wavelet filters as parameters in a probabilistic approach to texture analysis based on adaptive biorthogonal wavelet packet bases. The optimal choice for the mother wavelet filters is estimated from the data, in addition to the other model parameters. The model is applied to the classification of single texture images and mosaics of Brodatz textures, the results showing improvement over the performance of standard wavelets for a given filter length. |
|
136 - A Marked Point Process Model for Tree Crown Extraction in Plantations.
G. Perrin and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process.
@INPROCEEDINGS{perrin_icip05,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Marked Point Process Model for Tree Crown Extraction in Plantations}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Genoa, Italy}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.ps.gz}, |
| keyword |
= |
{Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process} |
| } |
Abstract :
This work presents a framework to extract tree crowns from remotely sensed data, especially in plantation images, using stochastic geometry. We aim at finding the tree top positions, and the tree crown diameter distribution. Our approach consists in considering that these images are some realizations of a marked point process. First we model the tree plantation as a configuration of an unknown number of ellipses. Then, a Bayesian energy is defined, containing both a prior energy which incorporates the prior knowledge of the plantation geometric properties, and a likelihood which fits the objects to the data. Eventually, we estimate the global minimum of this energy using Reversible Jump Markov Chain Monte Carlo dynamics and a simulated annealing scheme. We present results on optical aerial images of poplars provided by IFN. |
|
137 - Shape Moments for Region-Based Active Contours.
P. Horvath and A. Bhattacharya and I. H. Jermyn and J. Zerubia and Z. Kato. In Proc. Hungarian-Austrian Conference on Image Processing and Pattern Recognition, Szeged, Hungary, May 2005.
@INPROCEEDINGS{horvath_hacippr05,
|
| author |
= |
{Horvath, P. and Bhattacharya, A. and Jermyn, I. H. and Zerubia, J. and Kato, Z.}, |
| title |
= |
{Shape Moments for Region-Based Active Contours}, |
| year |
= |
{2005}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. Hungarian-Austrian Conference on Image Processing and Pattern Recognition}, |
| address |
= |
{Szeged, Hungary}, |
| url |
= |
{http://vision.vein.hu/HACIPPR/}, |
| keyword |
= |
{} |
| } |
|
138 - A Restoration Method for Confocal Microscopy Using Complex Wavelet Transform.
G. Pons Bernad and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Philadelphia, Pennsylvania, USA, March 2005.
@INPROCEEDINGS{pons_icassp2005,
|
| author |
= |
{Pons Bernad, G. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{A Restoration Method for Confocal Microscopy Using Complex Wavelet Transform}, |
| year |
= |
{2005}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Philadelphia, Pennsylvania, USA}, |
| pdf |
= |
{http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1415481}, |
| keyword |
= |
{} |
| } |
Abstract :
Confocal laser scanning microscopy is a powerful and increasingly popular technique for 3D imaging of biological specimens. However the acquired images are degraded by blur from out-of-focus light and Poisson noise due to photon-limited detection. Several deconvolution and/or denoising methods have been proposed to reduce these degradations.Here we propose a wavelet denoising method, which turns out to be very effective for three-dimensional confocal images. To obtain a translation and rotation invariant algorithm, we have developped the 3D Complex Wavelet Transform introduced by N. Kingsbury. These wavelets allow moreover a better directional selectivity of the wavelet coefficients. We show on simulated and real biological data the good performances of this algorithm. |
|
139 - Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory.
R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. Physics in Signal and Image Processing, Toulouse, France, January 2005.
Keywords : Bimodal, Statistics, Wavelet packet, Adaptive, Texture.
@INPROCEEDINGS{cossu_psip05,
|
| author |
= |
{Cossu, R. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory}, |
| year |
= |
{2005}, |
| month |
= |
{January}, |
| booktitle |
= |
{Proc. Physics in Signal and Image Processing}, |
| address |
= |
{Toulouse, France}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu05psip.pdf}, |
| keyword |
= |
{Bimodal, Statistics, Wavelet packet, Adaptive, Texture} |
| } |
Abstract :
In recent work, it was noted that although the subband histograms
for standard wavelet coefcients take on a generalized
Gaussian form, this is no longer true for wavelet packet
bases adapted to a given texture. Instead, three types of subband
statistics are observed: Gaussian, generalized Gaussian,
and interestingly, in some subbands, bi- or multi-modal histograms.
Motivated by this observation, we provide additional
experimental conrmation of the existence of multimodal
subbands, and provide a theoretical explanation for
their occurrence. The results reveal the connection of such
subbands with the characteristic structure in a texture, and
thus confirm the importance of such subbands for image modelling
and applications. |
|
140 - Texture discrimination using multimodal wavelet packet subbands.
R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004.
Keywords : Bimodal, Adaptive, probabilistic, Wavelet packet, Texture.
@INPROCEEDINGS{cossu_icip04,
|
| author |
= |
{Cossu, R. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture discrimination using multimodal wavelet packet subbands}, |
| year |
= |
{2004}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Singapore}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icip.pdf}, |
| keyword |
= |
{Bimodal, Adaptive, probabilistic, Wavelet packet, Texture} |
| } |
Abstract :
The subband histograms of wavelet packet bases adapted to individual
texture classes often fail to display the leptokurtotic behaviour
shown by the standard wavelet coefcients of `natural'
images. While many subband histograms remain leptokurtotic
in adaptive bases, some subbands are Gaussian. Most interestingly,
however, some subbands show multimodal behaviour, with
no mode at zero. In this paper, we provide evidence for the existence
of these multimodal subbands and show that they correspond
to narrow frequency bands running throughout images of the texture.
They are thus closely linked to the texture's structure. As
such, they seem likely to possess superior descriptive and discriminative
power as compared to unimodal subbands. We demonstrate
this using both Brodatz and remote sensing images. |
|
141 - Segmentation of remote sensing images by supervised TS-MRF.
G. Poggi and G. Scarpa and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004.
@INPROCEEDINGS{poggi_icip04,
|
| author |
= |
{Poggi, G. and Scarpa, G. and Zerubia, J.}, |
| title |
= |
{Segmentation of remote sensing images by supervised TS-MRF}, |
| year |
= |
{2004}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Singapore}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1421441}, |
| keyword |
= |
{} |
| } |
|
142 - Gap closure in (road) networks using higher-order active contours.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004.
Keywords : Active contour, Gap closure, Higher-order, Shape, Road network.
@INPROCEEDINGS{Rochery04,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Gap closure in (road) networks using higher-order active contours}, |
| year |
= |
{2004}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Singapore}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_icip04.pdf}, |
| keyword |
= |
{Active contour, Gap closure, Higher-order, Shape, Road network} |
| } |
Abstract :
We present a new model for the extraction of networks from images in the presence of occlusions. Such occlusions cause gaps in the extracted network that need to be closed. Using higher-order active contours, which allow the incorporation of sophisticated geometric information, we introduce a new, non-local, `gap closure' force that causes pairs of network extremities that are close together to extend towards one another and join, thus closing the gap
between them. We demonstrate the benefits of the model using the problem of road network extraction, presenting results on aerial images. |
|
143 - Texture analysis using adaptative biorthogonal wavelet packets.
G.C.K. Abhayaratne and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004.
Keywords : Adaptive, Wavelet packet, Biorthogonal, Texture, Statistics.
@INPROCEEDINGS{Abhayratne_icip04,
|
| author |
= |
{Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture analysis using adaptative biorthogonal wavelet packets}, |
| year |
= |
{2004}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Singapore}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne04icip.pdf}, |
| keyword |
= |
{Adaptive, Wavelet packet, Biorthogonal, Texture, Statistics} |
| } |
Abstract :
We discuss the use of adaptive biorthogonal wavelet packet bases
in a probabilistic approach to texture analysis, thus combining the
advantages of biorthogonal wavelets (FIR, linear phase) with those
of a coherent texture model. The computation of the probability
uses both the primal and dual coefcients of the adapted biorthogonal
wavelet packet basis. The computation of the biorthogonal
wavelet packet coefcients is done using a lifting scheme, which
is very efficient. The model is applied to the classification of mosaics
of Brodatz textures, the results showing improvement over
the performance of the corresponding orthogonal wavelets. |
|
144 - Unsupervised line network extraction from remotely sensed images by polyline process.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004.
@INPROCEEDINGS{lacoste04b,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Unsupervised line network extraction from remotely sensed images by polyline process}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7079995}, |
| pdf |
= |
{http://www.eurasip.org/Proceedings/Eusipco/Eusipco2004/defevent/papers/cr1608.pdf}, |
| keyword |
= |
{} |
| } |
|
145 - A Discontinuity detector for building extraction from Digital Elevation Models by stochastic geometry.
M. Ortner and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004.
Note : this paper has received a Young Authors award
@INPROCEEDINGS{ortner04b,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Discontinuity detector for building extraction from Digital Elevation Models by stochastic geometry}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| note |
= |
{this paper has received a Young Authors award}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7079720}, |
| keyword |
= |
{} |
| } |
|
146 - Simultaneous structure and texture compact representation.
J.F. Aujol and B. Matei. In Proc. Advanced Concepts for Intelligent Vision Systems, Brussels, Belgium, September 2004.
@INPROCEEDINGS{jf_acivs,
|
| author |
= |
{Aujol, J.F. and Matei, B.}, |
| title |
= |
{Simultaneous structure and texture compact representation}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Advanced Concepts for Intelligent Vision Systems}, |
| address |
= |
{Brussels, Belgium}, |
| pdf |
= |
{http://www.math.u-bordeaux1.fr/~jaujol/PAPERS/acivscompression.pdf}, |
| keyword |
= |
{} |
| } |
|
147 - SAR amplitude probability density function estimation based on a generalized Gaussian scattering model.
G. Moser and J. Zerubia and S.B. Serpico. In Proc. SPIE Symposium on Remote Sensing, Maspalomas, Gran Canaria, Spain, September 2004.
@INPROCEEDINGS{moser2004a,
|
| author |
= |
{Moser, G. and Zerubia, J. and Serpico, S.B.}, |
| title |
= |
{SAR amplitude probability density function estimation based on a generalized Gaussian scattering model}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. SPIE Symposium on Remote Sensing}, |
| address |
= |
{Maspalomas, Gran Canaria, Spain}, |
| url |
= |
{http://dx.doi.org/10.1117/12.567853}, |
| keyword |
= |
{} |
| } |
|
148 - Finite mixture models and stochastic EM for SAR amplitude probability density function estimation based on a dictionary of parametric families.
G. Moser and J. Zerubia and S.B. Serpico. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage , USA, September 2004.
@INPROCEEDINGS{moser2004b,
|
| author |
= |
{Moser, G. and Zerubia, J. and Serpico, S.B.}, |
| title |
= |
{Finite mixture models and stochastic EM for SAR amplitude probability density function estimation based on a dictionary of parametric families}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, |
| address |
= |
{Anchorage , USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1368708}, |
| keyword |
= |
{} |
| } |
|
149 - Tree Crown Extraction using Marked Point Processes.
G. Perrin and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004.
Keywords : RJMCMC, Marked point process, Simulated Annealing, Tree Crown Extraction, Object extraction, Stochastic geometry.
@INPROCEEDINGS{perrin04a,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Tree Crown Extraction using Marked Point Processes}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_eusipco2004.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_eusipco2004.ps.gz}, |
| keyword |
= |
{RJMCMC, Marked point process, Simulated Annealing, Tree Crown Extraction, Object extraction, Stochastic geometry} |
| } |
Abstract :
In this paper we aim at extracting tree crowns from remotely sensed images. Our approach is to consider that these images are some realizations of a marked point process. The first step is to define the geometrical objects that design the trees, and the density of the process.
Then, we use a Reversible Jump Markov Chain Monte Carlo dynamics and a simulated annealing to get the maximum a posteriori estimator of the tree crown distribution on the image. Transitions of the Markov chain are managed by some specific proposition kernels.
Results are shown on aerial images of poplars provided by IFN. |
|
150 - Image Disocclusion Using a Probabilistic Gradient Orientation.
E. Villéger and G. Aubert and L. Blanc-Féraud. In Proc. International Conference on Pattern Recognition (ICPR), Cambridge, United Kingdom, August 2004.
@INPROCEEDINGS{Villeger04,
|
| author |
= |
{Villéger, E. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{Image Disocclusion Using a Probabilistic Gradient Orientation}, |
| year |
= |
{2004}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Cambridge, United Kingdom}, |
| pdf |
= |
{http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1334034}, |
| keyword |
= |
{} |
| } |
|
151 - A Reversible Jump MCMC sampler for building detection in image processing.
M. Ortner and X. Descombes and J. Zerubia. In Monte Carlo Methods and Quasi-Monte Carlo Methods, series Special Se, Juan les Pins (France), June 2004.
@INPROCEEDINGS{mcmcqmc,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Reversible Jump MCMC sampler for building detection in image processing}, |
| year |
= |
{2004}, |
| month |
= |
{June}, |
| booktitle |
= |
{Monte Carlo Methods and Quasi-Monte Carlo Methods}, |
| series |
= |
{Special Se}, |
| address |
= |
{Juan les Pins (France)}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F3-540-31186-6_23}, |
| keyword |
= |
{} |
| } |
|
152 - A Bayesian Geometric Model for Line Network Extraction from Satellite Images.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Quebec, Canada, May 2004.
@INPROCEEDINGS{lacoste04a,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{A Bayesian Geometric Model for Line Network Extraction from Satellite Images}, |
| year |
= |
{2004}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Montreal, Quebec, Canada}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1326607}, |
| keyword |
= |
{} |
| } |
|
153 - A $l^1$-unified variational framework for image restoration.
J. Bect and L. Blanc-Féraud and G. Aubert and A. Chambolle. In Proc. European Conference on Computer Vision (ECCV), Vol. LNCS 3024, pages 1--13, Ed. T. Pajdla and J. Matas, Publ. Springer, Prague, Czech Republic, May 2004.
@INPROCEEDINGS{eccv04,
|
| author |
= |
{Bect, J. and Blanc-Féraud, L. and Aubert, G. and Chambolle, A.}, |
| title |
= |
{A $l^1$-unified variational framework for image restoration}, |
| year |
= |
{2004}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. European Conference on Computer Vision (ECCV)}, |
| volume |
= |
{LNCS 3024}, |
| pages |
= |
{1--13}, |
| editor |
= |
{T. Pajdla and J. Matas}, |
| publisher |
= |
{Springer}, |
| address |
= |
{Prague, Czech Republic}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F978-3-540-24673-2_1}, |
| keyword |
= |
{} |
| } |
|
154 - Deconvolution in confocal microscopy with total variation regularization.
N. Dey and L. Blanc-Féraud and C. Zimmer and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB), pages 117--120, May 2004.
@INPROCEEDINGS{Dey04b,
|
| author |
= |
{Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Deconvolution in confocal microscopy with total variation regularization}, |
| year |
= |
{2004}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB)}, |
| pages |
= |
{117--120}, |
| url |
= |
{http://www3.jouy.inra.fr/miaj/public/imaste/ssiab2004/program/abw92/}, |
| keyword |
= |
{} |
| } |
|
155 - Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients.
R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Canada, May 2004.
Keywords : Bimodal, Adaptive, Wavelet packet, Texture, Gaussian mixture, Statistics.
@INPROCEEDINGS{cossu04a,
|
| author |
= |
{Cossu, R. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients}, |
| year |
= |
{2004}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Montreal, Canada}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icassp.pdf}, |
| keyword |
= |
{Bimodal, Adaptive, Wavelet packet, Texture, Gaussian mixture, Statistics} |
| } |
Abstract :
Although subband histograms of the wavelet coefficients of
natural images possess a characteristic leptokurtotic form,
this is no longer true for wavelet packet bases adapted to
a given texture. Instead, three types of subband statistics
are observed: Gaussian, leptokurtotic, and interestingly, in
some subbands, multimodal histograms. These subbands
are closely linked to the structure of the texture, and guarantee
that the most probable image is not flat. Motivated by
these observations, we propose a probabilistic model that
takes them into account. Adaptive wavelet packet subbands
are modelled as Gaussian, generalized Gaussian, or a constrained
Gaussian mixture. We use a Bayesian methodology,
finding MAP estimates for the adaptive basis, for subband
model selection, and for subband model parameters.
Results confirm the effectiveness of the proposed approach,
and highlight the importance of multimodal subbands for
texture discrimination and modelling. |
|
156 - A deconvolution method for confocal microscopy with total variation regularization.
N. Dey and L. Blanc-Féraud and C. Zimmer and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Arlington, USA, April 2004.
Keywords : 3D confocal microscopy, Poisson deconvolution, total variation regularization.
@INPROCEEDINGS{Dey04a,
|
| author |
= |
{Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{A deconvolution method for confocal microscopy with total variation regularization}, |
| year |
= |
{2004}, |
| month |
= |
{April}, |
| booktitle |
= |
{Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, |
| address |
= |
{Arlington, USA}, |
| pdf |
= |
{http://dx.doi.org/10.1109/ISBI.2004.1398765}, |
| keyword |
= |
{3D confocal microscopy, Poisson deconvolution, total variation regularization} |
| } |
Abstract :
Confocal laser scanning microscopy is a powerful and increasingly popular technique for 3D imaging of biological specimens. However the acquired images are degraded by blur from out-of-focus light and Poisson noise due to photon-limited detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson-Lucy algorithm, which computes a maximum likelihood estimation adapted to Poisson statistics. However this method tends to amplify noise if used without regularizing constraint. Here, we propose to combine the Richardson-Lucy algorithm with a regularizing constraint based on total variation, whose smoothing avoids oscillations while preserving edges. We show on simulated images that this constraint improves the deconvolution result both visually and using quantitative measures. |
|
157 - Marked Point Process in Image Analysis : from Context to Geometry.
X. Descombes and F. Kruggel and C. Lacoste and M. Ortner and G. Perrin and J. Zerubia. In International Conference on Spatial Point Process Modelling and its Application (SPPA), Castellon, Spain, 2004.
Keywords : RJMCMC, Object extraction, Marked point process, Stochastic geometry.
@INPROCEEDINGS{geostoch04a,
|
| author |
= |
{Descombes, X. and Kruggel, F. and Lacoste, C. and Ortner, M. and Perrin, G. and Zerubia, J.}, |
| title |
= |
{Marked Point Process in Image Analysis : from Context to Geometry}, |
| year |
= |
{2004}, |
| booktitle |
= |
{International Conference on Spatial Point Process Modelling and its Application (SPPA)}, |
| address |
= |
{Castellon, Spain}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/SPPA_2004.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/SPPA_2004.ps.gz}, |
| keyword |
= |
{RJMCMC, Object extraction, Marked point process, Stochastic geometry} |
| } |
Abstract :
We consider the marked point process framework as a natural extension of the Markov random field approach in image analysis. We consider a general model defined by its density allowing us to consider some geometrical constraints on objects and between objects in feature extraction problems. Some examples are derived for small brain lesions detection from MR Images, road network, tree crown and building extraction from remotely sensed images. The results obtained on real data show the relevance of the proposal approach. |
|
158 - Extraction automatique de caricatures de bâtiments sur des Modèles Numériques d'Elèvation.
M. Ortner and X. Descombes and J. Zerubia. In Pixels et Cités, ENSG, Marne la Vallée, France, November 2003.
@INPROCEEDINGS{mathiaspix,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Extraction automatique de caricatures de bâtiments sur des Modèles Numériques d'Elèvation}, |
| year |
= |
{2003}, |
| month |
= |
{November}, |
| booktitle |
= |
{Pixels et Cités}, |
| address |
= |
{ENSG, Marne la Vallée, France}, |
| pdf |
= |
{Articles/PixelsCites2003.pdf}, |
| keyword |
= |
{} |
| } |
|
159 - Un Nouveau Modèle Pour L'extraction de Caricatures de Bâtiments sur Des Modèles Numériques D'Élèvation.
M. Ortner and X. Descombes and J. Zerubia. In Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA), Hammamet, Tunisia, October 2003.
@INPROCEEDINGS{mathiastaima,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Un Nouveau Modèle Pour L'extraction de Caricatures de Bâtiments sur Des Modèles Numériques D'Élèvation}, |
| year |
= |
{2003}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA)}, |
| address |
= |
{Hammamet, Tunisia}, |
| url |
= |
{http://conferences.telecom-bretagne.eu/taima/_2003/}, |
| keyword |
= |
{} |
| } |
|
160 - Wavelet-Based Superresolution in Astronomy.
R. Willett and I. H. Jermyn and R. Nowak and J. Zerubia. In Proc. Astronomical Data Analysis Software and Systems, Strasbourg, France, October 2003.
Keywords : Superresolution, Wavelets, Astronomy.
@INPROCEEDINGS{Willett03,
|
| author |
= |
{Willett, R. and Jermyn, I. H. and Nowak, R. and Zerubia, J.}, |
| title |
= |
{Wavelet-Based Superresolution in Astronomy}, |
| year |
= |
{2003}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. Astronomical Data Analysis Software and Systems}, |
| address |
= |
{Strasbourg, France}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Willett03adass.pdf}, |
| keyword |
= |
{Superresolution, Wavelets, Astronomy} |
| } |
Abstract :
High-resolution astronomical images can be reconstructed
from several blurred and noisy low-resolution images using a computational
process known as superresolution reconstruction. Superresolution
reconstruction is closely related to image deconvolution, except that the
low-resolution images are not registered and their relative translations
and rotations must be estimated in the process. The novelty of our approach
to the superresolution problem is the use of wavelets and related
multiresolution methods within an expectation-maximization reconstruction
process to improve the accuracy and visual quality of the reconstructed
image. Simulations demonstrate the effectiveness of the proposed
method, including its ability to distinguish between tightly grouped stars
with a small set of observations. |
|
161 - Higher Order Active Contours and their Application to the Detection of Line Networks in Satellite Imagery.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE Workshop Variational, Geometric and Level Set Methods in Computer Vision, at ICCV, Nice, France, October 2003.
Keywords : Higher-order, Active contour, Shape, Road network, Segmentation, Prior.
@INPROCEEDINGS{Rochery03a,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Higher Order Active Contours and their Application to the Detection of Line Networks in Satellite Imagery}, |
| year |
= |
{2003}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE Workshop Variational, Geometric and Level Set Methods in Computer Vision}, |
| address |
= |
{at ICCV, Nice, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_vlsm03.pdf}, |
| keyword |
= |
{Higher-order, Active contour, Shape, Road network, Segmentation, Prior} |
| } |
Abstract :
We present a novel method for the incorporation of shape information
into active contour models, and apply it to the extraction
of line networks (e.g. road, water) from satellite imagery.
The method is based on a new class of contour energies.
These energies are quadratic on the space of one-chains
in the image, as opposed to classical energies, which are linear.
They can be expressed as double integrals on the contour,
and thus incorporate non-trivial interactions between
different contour points. The new energies describe families
of contours that share complex geometric properties, without
making reference to any particular shape. Networks fall
into such a family, and to model them we make a particular
choice of quadratic energy whose minima are reticulated.
To optimize the energies, we use a level set approach. The
forces derived from the new energies are non-local however,
thus necessitating an extension of standard level set methods.
Promising experimental results are obtained using real
images. |
|
162 - Décomposition D'images: Application Aux Images RSO.
J.F. Aujol and G. Aubert and L. Blanc-Féraud and A. Chambolle. In Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, September 2003.
@INPROCEEDINGS{jf_gretsi,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, |
| title |
= |
{Décomposition D'images: Application Aux Images RSO}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Paris, France}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/13577}, |
| keyword |
= |
{} |
| } |
|
163 - Wavelet-Based Level Set Evolution for Classification of Textured Images.
J.F. Aujol and G. Aubert and L. Blanc-Féraud. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, September 2003.
@INPROCEEDINGS{jf_icip,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{Wavelet-Based Level Set Evolution for Classification of Textured Images}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Barcelona, Spain}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1246863}, |
| keyword |
= |
{} |
| } |
|
164 - Texture Analysis: An Adaptive Probabilistic Approach.
K. Brady and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, September 2003.
Keywords : Adaptive, Wavelet packet, Statistics, Texture.
@INPROCEEDINGS{Brady03,
|
| author |
= |
{Brady, K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture Analysis: An Adaptive Probabilistic Approach}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Barcelona, Spain}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Brady03icip.pdf}, |
| keyword |
= |
{Adaptive, Wavelet packet, Statistics, Texture} |
| } |
Abstract :
Two main issues arise when working in the area of texture
segmentation: the need to describe the texture accurately by
capturing its underlying structure, and the need to perform
analyses on the boundaries of textures. Herein, we tackle
these problems within a consistent probabilistic framework.
Starting from a probability distribution on the space of infinite
images, we generate a distribution on arbitrary finite
regions by marginalization. For a Gaussian distribution, the
computational requirement of diagonalization and the modelling
requirement of adaptivity together lead naturally to
adaptive wavelet packet models that capture the ‘significant
amplitude features’ in the Fourier domain. Undecimated
versions of the wavelet packet transform are used to diagonalize
the Gaussian distribution efficiently, albeit approximately.
We describe the implementation and application of
this approach and present results obtained on several Brodatz
texture mosaics. |
|
165 - Extraction de réseaux linéiques à partir d'images satellitaires par processus Markov objet.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, September 2003.
@INPROCEEDINGS{lacosteXDJZNB03,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Extraction de réseaux linéiques à partir d'images satellitaires par processus Markov objet}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Paris, France}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/13529}, |
| keyword |
= |
{} |
| } |
|
166 - Road Network Extraction in Remote Sensing by a Markov Object Process.
C. Lacoste and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, September 2003.
@INPROCEEDINGS{lacosteXDJZ03,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Road Network Extraction in Remote Sensing by a Markov Object Process}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Barcelona, Spain}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1247420}, |
| keyword |
= |
{} |
| } |
|
167 - Filtering interferometric phase images by anisotropic diffusion.
C. Lacombe and G. Aubert and L. Blanc-Féraud and P. Kornprobst. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, September 2003.
@INPROCEEDINGS{ClacombGALBFPK,
|
| author |
= |
{Lacombe, C. and Aubert, G. and Blanc-Féraud, L. and Kornprobst, P.}, |
| title |
= |
{Filtering interferometric phase images by anisotropic diffusion}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Barcelona}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1247201}, |
| keyword |
= |
{} |
| } |
|
168 - Étude D'une Nouvelle Classe de Contours Actifs Pour la Détection de Routes Dans Des Images de Télédétection.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, September 2003.
@INPROCEEDINGS{Rochery03,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Étude D'une Nouvelle Classe de Contours Actifs Pour la Détection de Routes Dans Des Images de Télédétection}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Paris, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_gretsi03.pdf}, |
| keyword |
= |
{} |
| } |
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